Why Google Cloud Platform?

Google is known for developing revolutionary technologies for its services, and once they have a better version, the old version is open-sourced to the world.

TensorFlow is one of these technologies. TensorFlow was Google’s AI engine and the core of Google Search. Now, it is open-sourced, and anyone can use it for Machine Learning tasks.

Let’s see why one should opt for the Google Cloud Platform.

Secure global private network#

Google Cloud is a large private network; nothing travels via the public Internet. When using Cloud CDN, traffic from your origin traverses through this proprietary fiber network for delivery through the location closest to the customer. Most research companies opt for GCP because no network is connected to the Internet.

Live migration of VMs#

Live migration is migrating your entire VM to other infrastructure, carrying out all the updates at the hardware level, restoring the VM without stopping it, and doing all this without any user intervention. As of now, live migration is only offered by Google Cloud. For other cloud providers, either you need to stop and migrate your VM for critical updates or create a new VM with a startup script.

Unconventional billing and offers#

Google Cloud introduced per-second billing to the market. Before that, it was either transaction-based billing or per-hour billing. A per-second billing model saves an individual a lot. Google Cloud also provides different discounts for some of the services.

• Sustained use discounts: Google Cloud provides up to a 30% discount if an eligible resource is used for the whole month.

• New users get a $300 credit for the first ninety days.

• Committed use discounts: If you are sure about the server usage, you can get up to an 80% discount by reserving the resource upfront, and equated monthly invoices are sent to you.

• Startup credits of $10000: If you are a startup, Google Cloud provides $ 10000 credits to use Google Cloud.

Creator of Kubernetes Engine#

Kubernetes is a container orchestration tool designed and created by Google to manage its infrastructure. Millions of containers were deployed to host Google services, and Kubernetes was the heart of those containers. Google open-sourced this technology and provided a Google-managed Kubernetes engine as a service.

Google Cloud powers Google’s infrastructure, and security is of the utmost concern to Google. Hence, highly skilled and certified professionals look after the infrastructure.

AI and ML services#

No question that Google is the king of data. The more data you have, the more accurate your model will produce in the AI world. Google’s ML services are the best. Google provides several ML services to train your model and handle the model training pipeline.

Kubeflow is trending for ML ops now. Again, KubeFlow was based on Google’s internal method to deploy TensorFlow models to Kubernetes, called TensorFlow Extended.

Organized structure#

Google Cloud services are designed so that any newbie can learn it quickly. In addition, the structure of services is very organized, which helps keep the services in different logical boundaries confined to different roles and levels of an organization.

These are some of the main factors that I think you should consider before opting for a cloud.

Apart from these, there are other reasons:

• Educative.io also uses Google Cloud.
• Google Cloud also offers lifetime-free resources. Check out cloud.google.com/free.
• Proven Google apps and services like maps hosted on Google Cloud.
• Google Cloud data centers run on half the energy of a typical data center and run on 100% renewable energy when available.

These reasons will help you answer, “Why GCP?” In the next lesson, we will start working on GCP. I suggest you have a spare email ID or create a new one to use the Google Cloud Platform.